Data Scientist, R&D

Church & Dwight Co., Inc.•Princeton, NJ

2d•$71,000 - $101,100•Hybrid

About The Position

Church & Dwight is seeking a Data Scientist to join its R&D Agentic Pod team, which designs, builds, deploys, and scales AI-powered products that transform how R&D teams make decisions and operate. The role applies statistical and machine learning expertise alongside R&D process knowledge to address challenges across consumer insights, product development, packaging, manufacturing processing, quality, and regulatory. It develops predictive, prescriptive, optimization, measurement, and performance-monitoring solutions; translates complex data into practical recommendations; and embeds analytical outputs into decision-support products and AI-enabled workflows. The Data Scientist works with R&D, Quality, Regulatory, Consumer Insights, AI Champions, GTA partners, analytics leaders, data engineering teams, and AI Engineers across the solution lifecycle, from opportunity identification and model development through deployment, adoption, measurement, and improvement. Success is measured by analytical rigor, model quality, the usefulness of recommendations, user adoption, and measurable commercial impact. The position is part of the Global R&D Innovation & AI organization and reports to the Lead, R&D Agentic Pod. This is a hybrid role based in Princeton, New Jersey. In-person attendance is required Tuesdays, Wednesdays, and Thursdays; remote work is available Mondays and Fridays.

Requirements

Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Econometrics, Analytics, Operations Research, or a related quantitative discipline.
At least 3 years of experience in data science, advanced analytics, machine learning, decision science, or a related field.
Demonstrated experience applying statistics, linear algebra, and machine learning to business problems.
Hands-on experience building and evaluating Python models using scikit-learn, XGBoost, or comparable frameworks.
Experience with SQL, large structured datasets, data preparation, feature engineering, model validation, and translating analysis into recommendations for non-technical stakeholders.
Knowledge of probability distributions, hypothesis testing, statistical inference, experimental design, causal measurement, supervised and unsupervised learning, forecasting, and optimization.
Action-oriented, data-driven problem-solving focused on measurable outcomes; strategic systems thinking and strong technical, business, and product acumen; collaborative, curious, and accountable approach, including effectiveness in ambiguity and influencing without authority.

Nice To Haves

Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Econometrics, Analytics, Operations Research, or a related quantitative discipline.
Experience with R&D, Quality, or Regulatory datasets and Consumer Packaged Goods (CPG).

Responsibilities

Partner with R&D, Quality, Regulatory, and Consumer Insights teams to frame business questions, define decision criteria, identify high-value analytics opportunities, and prioritize opportunities based on business value, analytical feasibility, data readiness, scalability, risk, and strategic alignment.
Develop predictive, prescriptive, segmentation, recommendation, propensity, forecasting, causal, experimental, and optimization solutions using structured and unstructured data.
Build, validate, maintain, and monitor machine learning models in Python using scikit-learn, XGBoost, or comparable frameworks.
Define features, assumptions, validation methods, performance metrics, and monitoring plans; assess bias, stability, explainability, failure modes, and business relevance.
Design test-and-learn and measurement frameworks to quantify incremental impact and translate findings into recommendations, scenarios, and decision tools.
Create proofs of concept and minimum viable analytical products, and embed models and measurement logic into dashboards, applications, agents, and other decision-support experiences.
Partner with AI Engineers, GTA, Enterprise Data Platform, and data engineering teams to productionize solutions and enable reliable, reusable, analysis-ready datasets.
Identify data quality, coverage, and availability gaps and recommend practical remediation approaches or viable analytical alternatives.
Document methodologies, assumptions, limitations, data lineage, and intended use; ensure compliance with data governance, privacy, security, and responsible AI requirements.
Drive adoption through stakeholder communication, training, reusable best practices, user feedback, and continuous improvement.